141 research outputs found
Can artificial intelligence mitigate environmental inequality?:Evidence from leading robotic-driven economies using quantile-based methods
No full textThe rapid integration of AI into global economies raises critical questions about its environmental impact, especially concerning the equitable distribution of carbon emissions. This aspect of climate justice remains largely unexplored. Existing literature extensively examines AI's aggregate environmental impacts; however, it largely overlooks the differential effects across emission quantiles and their implications for environmental inequality. This oversight creates a significant research gap. This study addresses this critical void by investigating the dynamic relationship between AI adoption and carbon emissions inequality (CEI) across ten technologically advanced economies from 2000 to 2023, employing a novel multivariate quantile-on-quantile regression (MQQR) methodology that captures nonlinear dependencies and heterogeneous effects across the entire distribution of environmental inequality. The empirical findings reveal stark heterogeneity across nations and quantile distributions, demonstrating the complex, non-uniform nature of AI's environmental equity implications. European economies exhibit a three-phase trajectory: early AI adoption increases emissions inequality; mid-level adoption stabilizes it; and advanced integration reduces it, particularly in Denmark, France, and Sweden. Germany displays persistent positive effects across all quantiles. Among Asian economies, Japan and Singapore transition from initial increases to reductions in emissions inequality at higher quantiles, while Korea shows consistently positive effects. China presents an oscillating pattern, and the United States exhibits alternating effects across different quantiles. These results underscore the double-edged role of AI in environmental equity, revealing that the relationship between technological advancement and climate justice is neither universal nor linear. The findings provide crucial insights for policymakers, emphasizing the need for quantile-specific, country-tailored AI governance strategies that account for national developmental stages and distributional impacts to ensure AI-driven transformation contributes to environmental justice objectives.</p
Circular Pathways to Sustainability: Asymmetric Impacts of the Circular Economy on the EU’s Capacity Load Factor
No full textAmid escalating environmental crises—ranging from biodiversity loss to climate instability—the circular economy has emerged as a promising pathway to align economic growth with ecological limits. The objective of this study is to examine the asymmetric impact of a novel composite circular economy index (CEI)—constructed via entropy weighting—on the load capacity factor (LCF), a holistic sustainability metric, across 27 EU member states over 2010–2023. Employing the method of moments quantile regression (MMQR) and controlling for GDP, foreign direct investment, trade openness, employment, and population growth, the main findings indicate pronounced heterogeneity: positive CEI shocks yield a 1.219 percent increase in LCF at the 90th quantile versus just 0.229 percent at the 10th, revealing a “sustainability premium” for high-performing economies, while negative shocks inflict a −5.253 percent decline at the 90th quantile, exposing their greater vulnerability. Low-LCF countries, by contrast, display relative resilience to downturns, likely due to less entrenched circular systems. Panel Granger causality tests further reveal bidirectional feedback loops between LCF and economic growth, investment, and labor markets, alongside a unidirectional effect from trade openness to enhanced sustainability. These insights carry clear policy implications: high-LCF nations require safeguards against circularity backsliding, whereas low-LCF members need capacity-building to convert latent resilience into sustained gains—together forming a nuanced blueprint for achieving the EU’s 2050 climate-neutrality ambitions
Investigating the relationships among green technologies, financial development and ecological footprint levels in Algeria:Evidence from a novel Fourier ARDL approach
No full textMany recent initiatives have been introduced to enhance ecological sustainability by minimizing countries' ecological footprints (EF). The focus has been on achieving environmental footprint neutrality through the application of green technologies (GT) and financial development (FD) in facilitating this transition. To determine the contribution of these variables to sustainability, this study investigated the effects of GT and FD on EF in Algeria from Q1/1990 to Q4/2021. Additionally, this research examines the moderating role of GT with FD on EF. To achieve these objectives, advanced Fourier autoregressive distributed lag techniques and the Fourier causality test were employed. The findings reveal that FD increases EF, leading to ecological degradation. Conversely, GT reduces EF in the long run, demonstrating its potential to foster ecological sustainability. Notably, the study highlights the significant moderating role of GT in the FD-EF relationship. This underscores the critical role of environmental technologies in mitigating the adverse effects of FD by facilitating creative technologies and lowering EF. Therefore, the study recommends that Algeria integrates GT with FD to achieve long-term reduction of environmental harm. In conclusion, Algeria needs to hasten GT in combination with stronger FD to mitigate ecological impacts without compromising sustainable economic growth.</p
Towards eco-efficiency of OECD countries: How does environmental governance restrain the destructive ecological effect of the excess use of natural resources?
No full textDeveloped economies face mounting environmental challenges from excessive resource consumption, but we lack clear evidence on how environmental policies can best address these issues. This study investigates how environmental governance shapes resource use and ecological efficiency across nine OECD countries from 1997 to 2020. Our analysis reveals that stronger environmental policies significantly improve eco-efficiency: a 1 % increase in environmental governance effectiveness enhances eco-efficiency by 0.65–0.95 %, with the strongest effects observed in countries currently showing lower ecological efficiency. We find that increasing energy transition efforts and research and development investment each contribute to improved eco-efficiency (0.07–0.11 % and 0.19–0.35 % respectively), while excessive resource use reduces it by 0.07–0.03 %. Notably, our study introduces a novel analytical approach by examining how environmental policies moderate the negative impacts of resource overuse across different levels of ecological efficiency. This relationship proves especially important for countries struggling with lower eco-efficiency, where strong environmental governance can effectively offset the harmful effects of excessive resource consumption. These findings remain consistent across multiple measures of eco-efficiency and trade indicators, offering robust evidence for policymakers. Our research provides practical guidance for balancing economic development with environmental protection through targeted policy interventions, particularly in resource-intensive economies working to improve their ecological performance. © 2025 The Author(s
Industrial robots for a sustainable future:Uncovering the asymmetric effects of AI on ecological quality in G7 economies
No full textArtificial intelligence is increasingly recognized for its potential to enhance ecological quality by streamlining production processes, reducing environmental emissions, and improving ecological monitoring systems. However, the influence of artificial intelligence on ecological quality is neither uniform across different stages of technological adoption nor consistent across national contexts. The central objective of this study is to investigate the asymmetric and stage-specific effects of artificial intelligence adoption on ecological quality within the Group of Seven (G7) economies over the period from January 2000 to December 2019. Employing a novel multivariate quantile-on-quantile regression framework, this research examines how varying intensities of artificial intelligence adoption impact different levels of ecological outcomes. The results indicate that artificial intelligence exerts a modest positive effect on ecological quality during early stages of adoption, a more substantial effect during transitional phases, and a significantly positive influence at advanced stages of integration. To address endogeneity concerns—particularly reverse causality and omitted variable bias—this study utilizes an instrumental variable multivariate quantile regression approach, using lagged values of artificial intelligence adoption as an instrument. The findings are validated through robustness checks using kernel regularized least squares and standard quantile regression techniques. The results also reveal considerable variation across countries, highlighting the necessity for country-specific and stage-aware policy interventions. Accordingly, the study offers detailed, actionable recommendations tailored to the adoption stage of each G7 member to maximize the ecological benefits of artificial intelligence. This research provides a rigorous, causally grounded analysis of how artificial intelligence can be harnessed to advance environmental sustainability in highly industrialized economies.</p
Algeria's pathway to COP28 and SDGs:Asymmetric impact of environmental technology, energy productivity, and material resource efficiency on environmental sustainability
No full textThis study examines the asymmetric effects of material resource efficiency (MRE), energy productivity (EP), and environmental technology (ENT) on environmental sustainability (ES) in Algeria from 1990/Q1 to 2022/Q4. Using a nonlinear autoregressive distributed lag model (NARDL), we find that both positive and negative shocks to both MRE and ENT contribute to ES, highlighting their effectiveness. Interestingly, positive EP shocks have a stronger mitigating effect compared to negative shocks. This suggests a magnified benefit during periods of efficiency gains. Additionally, asymmetric causality analysis verifies a causal link between MRE, EP, ENT, and ES. Given these results, it is recommended that Algerian policymakers should prioritize investments in MRE and ENT while emphasizing policies that enhance EP due to its magnified benefit on ES. Asymmetric policy responses for green practices alongside alignment with SDGs 7, 8, 12 and COP-28 objectives will optimize Algeria's ES.</p
Moving toward environmental mitigation in Algeria:Asymmetric impact of fossil fuel energy, renewable energy and technological innovation on CO2 emissions
No full textAlgeria's recent economic shifts have caused its macroeconomic data to exhibit an abnormal distribution, requiring a nonlinear approach to examine the asymmetric impact of technological innovation (TI), fossil fuel energy (FFE), and renewable energy (RE) on CO2 emissions. This study employs the nonlinear autoregressive distributed lag (NARDL) model to analyze the asymmetric impact of these factors on CO2 emissions. Furthermore, Quantile Autoregressive Distributed Lag (QARDL) and Quantile Granger Causality (QGC) approaches are employed for robustness checks. The NARDL results indicate that positive shocks in TI decrease CO2 emissions, whereas negative shocks increase CO2 emissions. Positive shocks in RE also decrease CO2 emissions, while negative shocks have no effect. In contrast, positive shocks in FFE increase CO2 emissions, but negative shocks have an even stronger effect, resulting in almost double CO2 emissions over time. These findings confirm the presence of asymmetry, as positive and negative shocks in regressors clearly influence CO2 emissions in Algeria. Moreover, the results from the asymmetric causality analysis indicate that TI, RE, and FFE have a causal effect on CO2 emissions in Algeria. These results are consistent with the findings from the QARDL and QGC approaches. Therefore, it is crucial for Algeria to prioritize investment in sustainable technology and implement carbon-neutral energy policies to reduce its reliance on fossil fuel and encourage the use of cleaner energy sources. The shift towards a green energy sector requires policymakers to ensure that innovation aligns with development objectives.</p
Institutional adaptability, skill-bias technological shifts, and energy efficiency in global decarbonization pathways:Exploring the role of artificial intelligence patents
No full textThe catalytic role of artificial intelligence patents (AIP) in accelerating global decarbonization pathways—through structural reductions in CO2 emissions—remains underexplored. Leveraging a panel of 29 countries (2005–2023), this study explores the effect of AIP on carbon mitigation. We find robust evidence that AIP drives significant emissions reductions, with results persisting across instrumental variable regressions, sensitivity analyses, and alternative model specifications. Mechanism tests reveal three decarbonization channels: AIP drives technological shift by favoring skilled labor and automating carbon-intensive routine tasks; institutional adaptability magnifies these decarbonization benefits; and AIP enhances systemic energy efficiency, evidenced by substantial reductions in both primary energy intensity and per capita CO2 emissions. Heterogeneity tests demonstrate that AIP's decarbonization impact is most pronounced in economies geographically proximate to the USA (the global AI innovation hub), in lower-income nations, and in countries with high fossil fuel dependency. Within AIP categories, energy-management patents yield the highest decarbonization returns. These findings underscore AI patents as critical yet underutilized tools for achieving net-zero transitions, with policy implications for targeted intellectual property incentives, global technology transfer, and institutional reforms to amplify decarbonization synergies.</p
New Evidence on the Oil-Democracy Nexus Utilising the Varieties of Democracy Data
No full textThis study re-examines the oil and democracy nexus, which is central to the political resource curse by applying the latest democracy dataset, VDEM, into the analysis in a sample of 100 Developing Countries, over the period 1935-2014. Our study is a contribution to this taxonomic literature where we improve on previous studies not only by employing a novel democratic data source, but also because we use two definitions of oil wealth which renders our results more robust, besides delineating the sample into small and large oil endowments and looking into the experience of two regions, Latin America and the Middle East. Our analysis highlights nuances in the oil-democracy relationship. First, that there is prima facie evidence for a political resource curse if wedo not control for pre-existing institutions that promote democracy. Second, once we decompose the sample into small and large oil endowments, the political resource curse vanishes, and also for Latin America, whilst for oil dependent economies in the Middle East and North Africa it still remains. Third, after controlling for pre-existing institutional quality, measured in our case by the rule of law, chances of the political resource curse seem to diminish. We also calculate threshold levels for the quality of the rule of law to be at in society before they turn a curse into a blessing. The converse is equally true, a deterioration in the quality and pervasiveness of the rule of law willcause the political resource curse to reappear, and democratic quality will decline
Assessing the Impact of Green Energy Transition, Technological Innovation, and Natural Resources on Load Capacity Factor in Algeria:Evidence from Dynamic Autoregressive Distributed Lag Simulations and Machine Learning Validation
No full textAlgeria’s resource-dependent economy faces significant challenges in balancing hydrocarbon reliance with environmental sustainability, yet existing research largely overlooks the comprehensive load capacity factor (LCF) metric in favor of traditional emissions analyses. This study examines the relationships between the LCF and key economic–environmental factors in Algeria from 1980 to 2023, including total natural resource rents, energy transition, technological innovation, GDP, primary energy consumption, and urbanization. Using ARDL and DARDL econometric approaches complemented by a kernel-based regularized least squares analysis, the research captures both linear and nonlinear relationships while accounting for asymmetric dynamics in short- and long-term perspectives. The findings reveal that natural resource rents, technological innovation, and urbanization significantly impair Algeria’s LCF, while primary energy consumption shows a minimal positive impact. The energy transition initiatives demonstrate mixed effects, highlighting the complexities of green energy implementation in resource-dependent economies. These results suggest that Algeria’s sustainable development requires targeted policies focusing on resource management efficiency, environmentally conscious urban planning, and green technology adoption, providing valuable insights for other resource-rich nations pursuing similar sustainability transitions.</p
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